Communication regarding the article “An efficient primary screening COVID-19 by serum Raman spectroscopy”

When performing computational modeling and machine learning experiments, it is imperative to follow a protocol that minimizes bias. In this communication, we share our concerns regarding the article “An efficient primary screening COVID-19 by serum Ramana spectroscopy” published in this journal. We consider that the authors may have inadvertently biased their results by not guaranteeing complete independence of test samples from the training data. We corroborate our point by reproducing the experiment with the available data, showing that if full independence of the test set was ensured, the reported results should be lower. We ask the authors to provide more information regarding their article, as well as making available all code used to generate their results.<br>Revision 2 contains additional experiments after the first round of peer-review.

在开展计算建模（computational modeling）与机器学习（machine learning）实验时，必须遵循能够最大限度降低偏倚的实验方案。在本通讯中，我们针对本刊刊载的论文《基于血清Ramana光谱的新型冠状病毒肺炎（COVID-19）高效初筛》提出相关质疑。
我们认为，作者因未能确保测试样本与训练数据完全独立，可能在无意间使研究结果产生偏倚。我们通过现有公开数据复现实验佐证了这一观点，结果表明：若确保测试集与训练集完全独立，论文所报道的实验结果理应更低。
我们恳请作者就该论文提供更多相关信息，并公开所有用于生成其研究结果的代码。修订版2包含了首轮同行评议后新增的实验内容。